What a 70% Support Automation Target Requires
A worked planning example that shows the content, routing, review, and measurement needed to pursue a 70% support automation target.
- Updated
- Reading time
- 6 min read
8 sections
- Start with the ticket mix
- Define what counts as automated
- Model a conservative first month
Worked example: StreamlineOps is fictional. Every number below is an assumption for planning, not a Chatsy customer result or a performance promise.
Suppose a small B2B software company receives 2,000 support conversations each month. Its team wants to see whether automating 70% is plausible without hiding the path to a person.
The target sounds simple. The work is not. A team must have repeat questions, accurate source material, safe routing rules, and a way to review conversations. If one of those pieces is missing, the target should come down.
Start with the ticket mix
StreamlineOps audits a month of conversations and assigns each one to a category.
| Category | Share of conversations | Initial decision |
|---|---|---|
| Product how-to questions | 30% | Test with the agent |
| Account and billing questions | 20% | Answer general policy only |
| Access problems | 15% | Guide the customer, then hand off |
| Technical troubleshooting | 20% | Triage and collect context |
| Complaints, refunds, and exceptions | 15% | Send to a person |
These shares are assumptions. Your own audit should replace them.
At this point, 65% of the example volume is eligible for some automation. Eligible does not mean resolved. It only means the team is willing to test the category.
Define what counts as automated
StreamlineOps counts a conversation as automated only when all of the following are true:
- The customer received an answer grounded in approved content.
- The customer did not request a person.
- No teammate had to correct the answer.
- The conversation did not reopen within the team's review window.
An instant first reply does not count as a resolution. Neither does a conversation that the bot blocks from reaching a person.
Model a conservative first month
The team uses these planning inputs:
| Input | Assumption |
|---|---|
| Monthly conversations | 2,000 |
| Share eligible for testing | 65% |
| Containment within eligible categories | 60% |
| Average human handling cost | $6 |
| Monthly software cost used in the model | $900 |
The calculation is:
- Eligible conversations: 2,000 x 65% = 1,300
- Modeled automated resolutions: 1,300 x 60% = 780
- Modeled automation rate: 780 / 2,000 = 39%
- Modeled gross handling cost avoided: 780 x $6 = $4,680
- Modeled net monthly saving: $4,680 - $900 = $3,780
This is a forecast. It is not an observed result. The useful finding is that a 70% target is not supported by the first set of assumptions.
What would need to change to reach 70%
Seventy percent of 2,000 conversations is 1,400 automated resolutions. The first model produces 780, leaving a gap of 620.
StreamlineOps should not close that gap by counting weak answers as resolved. It has only a few honest options:
- Fix product and billing problems that create avoidable tickets.
- Add missing help content for repeated questions.
- Connect approved actions only where identity and permissions are clear.
- Improve answer quality in categories already approved for automation.
- Keep exceptions and sensitive conversations with people.
If the remaining volume is mostly unique technical work or account exceptions, 70% may be the wrong goal.
A rollout with evidence gates
Audit before configuring
Export recent conversations, remove spam, and group the real customer questions. Read examples from every large category. Ticket tags alone are often too broad.
Record the baseline:
- Conversation volume
- First response time
- Resolution time
- Reopen rate
- Customer satisfaction
- Human handling time
Build the first source set
Start with the categories that have stable, written answers. Remove duplicate articles and old policy pages. Assign an owner to each source so updates do not become nobody's job.
Do not give the agent permission to invent a policy when the source is silent.
Test privately
Ask the agent real questions from the audit. Include typos, follow-up questions, missing context, and requests that should go to a person.
For each test, record:
- Correct answer
- Incomplete answer
- Wrong answer
- Safe handoff
- Unsafe or blocked handoff
Do not publish because a polished demo question worked.
Release one category at a time
Publish the best-understood category first. Review its conversations before adding another. A team should know why an answer failed before increasing traffic.
Pause a category when policy changed, source content conflicts, or the agent repeatedly needs correction.
The scorecard
Review automation alongside quality. One number can hide a lot of bad support.
| Metric | Why it matters |
|---|---|
| Verified automation rate | Shows resolutions that met the team's definition |
| Correction rate | Finds answers teammates had to repair |
| Reopen rate | Catches false resolutions |
| Handoff completion | Shows whether customers actually reached a person |
| Customer satisfaction | Measures the customer's view of the interaction |
| Cost per verified resolution | Tests whether the program saves money |
Break these metrics down by category. An overall rate can look healthy while billing answers fail.
What this example does not prove
The StreamlineOps model does not prove that Chatsy will automate 70% of your support. It does not prove a four-week implementation, a specific response time, or a fixed cost reduction.
It does show how to test the target without turning assumptions into a case study. Use your own ticket mix, costs, and quality thresholds in the ROI calculator.
Frequently asked questions
Is 70% a good automation target?
Only if your conversation data supports it. Teams with a large share of repeated, documented questions may have more room to automate. Teams handling custom technical work may have much less.
How long should a rollout take?
There is no reliable fixed timeline. Move forward when the current category meets your quality and handoff thresholds. Stop when it does not.
Which conversations should stay with people?
Keep complaints, refunds, legal or security issues, account exceptions, and unclear high-impact decisions with a person unless your team has built and reviewed a specific workflow.
What should we test first?
Start with a high-volume category that has stable source material and low risk. Product navigation and basic how-to questions are often easier to review than billing exceptions.